Design and kinematical performance analysis of the 7-DOF upper-limb exoskeleton toward improving human-robot
Qiaoling Meng1,2,3, Cuizhi Fei1,2,3, Zongqi Jiao1,2,3
1Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China.
Background:
Upper-limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. The design of exoskeleton mechanisms plays a key role to improve human-robot interface in the upper-limb movements under passive and active rehabilitation training.
Objective:
This paper proposes a novel of the 7-DOF (RR-RR-PRR) under-actuated exoskeleton mechanism based on the characteristics of the upper-limb movements in both of active and passive training. This aim of the proposed work is to improve human-robot interface in rehabilitation training with robots.
Methods:
Firstly, the characteristics of active and passive movement training are analyzed depending on the human upper-limb model. Then, a novel 7-DOF (RR-RR-PRR) exoskeleton mechanism is proposed based on the analyzed characteristics. After that, kinematical performances of the proposed exoskeleton are analyzed on the workspace, manipulability and manipulability ellipsoid by compared with the common exoskeleton configuration of the 7 DOFs (RRR-R-PRR) mechanism. In the end, the prototype is manufactured and tested by undergoing the experiments of single-joint passive movement training and multi-joint active movement training. The human-robot interface of the proposed exoskeleton is demonstrated by root mean square error, Pearson correlation coefficient, and the time-delay difference.
Results:
The results of the kinematical performance show that the effective workspace and the flexibility of the exoskeleton with the proposed configuration are increased by 10.44% and 1.7%. In the single-joint passive movement training experiment, the root mean square errors are 6.986, 7.568, 5.846, and Pearson correlation coefficients are 0.989, 0.984, 0.988 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.1%. In the multi-joint active movement training experiment, the root mean square errors are 9.312 and 7.677, and Pearson correlation coefficients are 0.906 and 0.968 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.28%.
Conclusions:
The proposed 7 DOFs exoskeleton mechanism shows uniformity with that of the common exoskeleton on the same rehabilitation trajectory which is effective to improve human-robot interface under passive and active rehabilitation training.
